i_mca | R Documentation |

This function computes the Multiple Correspondence Analysis (MCA) solution on the indicator matrix using two incremental methods described in Iodice D'Enza & Markos (2015)

i_mca(data1, data2, method=c("exact","live"), current_rank, nchunk = 2, ff = 0, disk = FALSE)

`data1` |
Matrix or data frame of starting data or full data if data2 = NULL |

`data2` |
Matrix or data frame of incoming data |

`method` |
String specifying the type of implementation: |

`current_rank` |
Rank of approximation or number of components to compute; if empty, the full rank is used |

`nchunk` |
Number of incoming data chunks (equal splits of 'data2', |

`ff` |
Number between 0 and 1 indicating the "forgetting factor" used to down-weight the contribution of earlier data blocks to the current solution. When |

`disk` |
Logical indicating whether then output is saved to hard disk |

`rowpcoord` |
Row principal coordinates |

`colpcoord` |
Column principal coordinates |

`rowcoord` |
Row standard coordinates |

`colcoord` |
Column standard coordinates |

`sv` |
Singular values |

`inertia.e` |
Percentages of explained inertia |

`levelnames` |
Column labels |

`rowctr` |
Row contributions |

`colctr` |
Column contributions |

`rowcor` |
Row squared correlations |

`colcor` |
Column squared correlations |

`rowmass` |
Row masses |

`colmass` |
Column masses |

`nchunk` |
A copy of |

`disk` |
A copy of |

`ff` |
A copy of |

`allrowcoord` |
A list containing the row principal coordinates produced after each data chunk is analyzed; returned only when |

`allcolcoord` |
A list containing the column principal coordinates on the principal components produced after each data chunk is analyzed; returned only when |

`allrowctr` |
A list containing the row contributions after each data chunk is analyzed; returned only when |

`allcolctr` |
A list containing the column contributions after each data chunk is analyzed; returned only when |

`allrowcor` |
A list containing the row squared correlations produced after each data chunk is analyzed; returned only when |

`allcolcor` |
A list containing the column squared correlations produced after each data chunk is analyzed; returned only when |

Hall, P., Marshall, D., & Martin, R. (2002). Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. *Image and Vision Computing*, *20*(13), 1009-1016.

Iodice D' Enza, A., & Markos, A. (2015). Low-dimensional tracking of association structures in categorical data, *Statistics and Computing*, *25*(5), 1009–1022.

Iodice D'Enza, A., Markos, A., & Buttarazzi, D. (2018). The idm Package: Incremental Decomposition Methods in R. *Journal of Statistical Software, Code Snippets*, *86*(4), 1–24. DOI: 10.18637/jss.v086.c04.

Ross, D. A., Lim, J., Lin, R. S., & Yang, M. H. (2008). Incremental learning for robust visual tracking. *International Journal of Computer Vision*, *77*(1-3), 125–141.

`update.i_mca`

, `i_pca`

, `update.i_pca`

, `add_es`

##Example 1 - Exact case data("women", package = "idm") nc = 5 # number of chunks res_iMCAh = i_mca(data1 = women[1:300,1:7], data2 = women[301:2107,1:7] ,method = "exact", nchunk = nc) #static MCA plot of attributes on axes 2 and 3 plot(x = res_iMCAh, dim = c(2,3), what = c(FALSE,TRUE), animation = FALSE) #\donttest is used here because the code calls the saveLatex function of the animation package #which requires ImageMagick or GraphicsMagick and #Adobe Acrobat Reader to be installed in your system #Creates animated plot in PDF for objects and variables plot(res_iMCAh, animation = TRUE, frames = 10, movie_format = 'pdf') ##Example 2 - Live case data("tweet", package = "idm") nc = 5 #provide attributes with custom labels labels = c("HLTN", "ICN", "MRT","BWN","SWD","HYT","CH", "-", "-/+", "+", "++", "Low", "Med","High") #mimics the 'live' MCA implementation res_iMCAl = i_mca(data1 = tweet[1:100,], data2 = tweet[101:1000,], method="live", nchunk = nc, current_rank = 2) #\donttest is used here because the code calls the saveLatex function of the animation package #which requires ImageMagick or GraphicsMagick and #Adobe Acrobat Reader to be installed in your system #See help(im.convert) for details on the configuration of ImageMagick or GraphicsMagick. #Creates animated plot in PDF for observations and variables plot(res_iMCAl, labels = labels, animation = TRUE, frames = 10, movie_format = 'pdf')

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.